Deep Mind Demis Hassabis and the Future of AI

Demis Hassabis is the CEO and co-founder of DeepMind and he had a great talk with Lex Fridman about AI.

DeepMind beat the best human players with Go and they also made the best AI for chess. DeepMind also created AlphaFold 2 which has solved protein folding.

As advanced mathematics and Calculus were critical to the progress of Physics it appears that AI will be able to accelerate the advance the science of Biology.

Below an article in Nature describes the impact and current limitations of Alphafold 2. Alphafold 2 and researchers will need to work together to test more proteins and generate more data. More data will help improve Alphafold 2 and its predictive accuracy.

There is still work to do but this is improving drug discovery using proteins and it could help crack five grand challenges that are possible with designed proteins.

It seems like DeepMind software is very powerful as a tool for areas with large amounts of data and complexity.

They have also been able to use it forecast how to adjust magnetic fields to contain plasma for nuclear fusion.

Above – A top-down view of the human nuclear pore complex, the largest molecular machine in human cells. Credit: Agnieszka Obarska-Kosinska

Nature – What’s next for AlphaFold and the AI protein-folding revolution.

DeepMind software that can predict the 3D shape of proteins is already changing biology.

For more than a decade, molecular biologist Martin Beck and his colleagues have been trying to piece together one of the world’s hardest jigsaw puzzles: a detailed model of the largest molecular machine in human cells.

This behemoth, called the nuclear pore complex, controls the flow of molecules in and out of the nucleus of the cell, where the genome sits. Hundreds of these complexes exist in every cell. Each is made up of more than 1,000 proteins that together form rings around a hole through the nuclear membrane.

These 1,000 puzzle pieces are drawn from more than 30 protein building blocks that interlace in myriad ways. Making the puzzle even harder, the experimentally determined 3D shapes of these building blocks are a potpourri of structures gathered from many species, so don’t always mesh together well. And the picture on the puzzle’s box — a low-resolution 3D view of the nuclear pore complex — lacks sufficient detail to know how many of the pieces precisely fit together.

July 2021, DeepMind, part of Alphabet — Google’s parent company — made public an artificial intelligence (AI) tool called AlphaFold2. The software could predict the 3D shape of proteins from their genetic sequence with, for the most part, pinpoint accuracy.

In some cases, the AI has saved scientists time; in others it has made possible research that was previously inconceivable or wildly impractical. It has limitations, and some scientists are finding its predictions to be too unreliable for their work. But the pace of experimentation is frenetic.

On 15 July 2021, papers describing RoseTTAFold and AlphaFold2 appeared2, along with freely available, open-source code and other information needed for specialists to run their own versions of the tools. A week later, DeepMind announced that it had used AlphaFold to predict the structure of nearly every protein made by humans, as well as the entire ‘proteomes’ of 20 other widely studied organisms, such as mice and the bacterium Escherichia coli — more than 365,000 structures in total.

This year, DeepMind plans to release a total of more than 100 million structure predictions. That is nearly half of all known proteins — and hundreds of times more than the number of experimentally determined proteins in the Protein Data Bank (PDB) structure repository.

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